Increasing productivity, lowering manufacturing costs, and improving on-time performance are common concerns for most manufacturing plants and factories. With increasing market pressure for rapid reaction to fluctuating demand, scheduling an efficient manufacturing process, which maximizes the utilization of various resources, and minimizes equipment changeovers and downtimes, has become increasingly challenging. Historically, problems relating to the planning of manufacturing schedules are resolved by skillful technicians and planners. The planning process of a manufacturing schedule typically requires a skillful planner to make various adjustments to the planning preferences to anticipate fluctuations in demand and/or unexpected events such as equipment failure and labor issues.
In the past, the production plan for a factory has typically been generated by a planner who heuristically applies his planning preferences regarding desirable grouping, etc. of the required production tasks. These planning preferences have usually been acquired by the planner during many years of experience at the factory. Such planning preferences are often vital intellectual property of a manufacturing company. A problem is that this critical know-how may be absent when the skillful planner is absent. Another problem is that, with the increasing complexity and faster pace of production planning, the skillful planner has a more difficult time to quickly and consistently apply and/or adjust the planning preferences.
Accordingly, there is a need in the art to quantify the adjustments of planning preferences to improve the planning process for manufacturing schedules.